Abstract
Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the Rank SVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.
Original language | English |
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Title of host publication | KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
Publisher | ACM |
Pages | 195-204 |
Number of pages | 10 |
Volume | 13-17-August-2016 |
ISBN (Electronic) | 9781450342322 |
DOIs | |
Publication status | Published - 13 Aug 2016 |
MoE publication type | A4 Article in a conference publication |
Event | ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Francisco, United States Duration: 13 Aug 2016 → 17 Aug 2016 Conference number: 22 |
Conference
Conference | ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
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Abbreviated title | KDD |
Country | United States |
City | San Francisco |
Period | 13/08/2016 → 17/08/2016 |